用于半主动压电调谐质量阻尼器分流参数辨识的可解释物理信息神经网络频率响应框架
An Explainable Physics-Informed Neural Frequency-Response Framework for Shunt-Parameter Identification in Semi-Active Piezoelectric Tuned Mass Dampers
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中文总结 AI 辅助
本文提出一种可解释物理信息神经网络频率响应框架，结合物理前向模型、神经逆学习模块与可解释组件，实现半主动压电调谐质量阻尼器分流参数的高效可辨识。
中文摘要 AI 辅助
本文提出一种物理信息神经网络频率响应框架，用于学习和解释半主动分流压电调谐质量阻尼器（STMD）的频域行为。研究动机在于，此类系统的行为最自然地通过频率响应函数表达，而控制机电相互作用强烈依赖于隐藏的结构参数和分流参数。传统数据驱动模型可近似这些映射，但往往缺乏物理一致性、需要大量训练数据集且可解释性有限。为解决这些局限，所提框架结合了基于物理的前向频率响应模型、神经逆学习模块及可解释性组件。前向模型用于在广泛的结构和分流配置范围内生成合成复数值频率响应数据，同时保留系统的控制机电行为。基于合成频率响应数据，训练神经逆模型以从频谱响应特征估计隐藏参数，随后使用独立测量的实验频率响应函数（FRF）对其进行评估。这种合成到实验的设计无需为每个测量案例求解新的优化问题即可实现快速参数推理。为提升对实际条件的鲁棒性，仅在逆训练阶段引入受控噪声，而底层物理模型保持无噪声状态。此外，通过潜在空间组织、敏感性映射及简化符号蒸馏分析学习到的表示，以提取可解释的机电响应描述符。所得框架为基于频率响应的STMD辨识和逆调谐提供了一种数据高效且可解释的机器学习方法。
英文摘要
This paper proposes a Physics-Informed Neural Frequency Response Framework for learning and interpreting the frequency-domain behavior of semi-active shunted piezoelectric tuned mass dampers. The motivation is that the behavior of such systems is most naturally expressed through frequency response functions, while the governing electromechanical interactions depend strongly on hidden structural and shunt parameters. Conventional data-driven models can approximate these mappings, but they often lack physical consistency, require large training datasets, and provide limited interpretability. To address these limitations, the proposed framework combines a physics-based forward frequency-response model, a neural inverse learning module, and an explainability component. The forward model is used to generate synthetic complex-valued frequency-response data over a broad range of structural and shunt configurations while preserving the governing electromechanical behavior of the system. Based on synthetic frequency-response data, the neural inverse model is trained to estimate hidden parameters from spectral response signatures and is subsequently evaluated using independently measured experimental FRFs. This synthetic-to-experimental design enables fast parameter inference without solving a new optimization problem for each measured case. To improve robustness to realistic conditions, controlled noise is introduced only at the inverse-training stage, while the underlying physics model remains noise-free. In addition, the learned representation is analyzed through latent-space organization, sensitivity mapping, and reduced symbolic distillation in order to extract interpretable electromechanical response descriptors. The resulting framework provides a data-efficient and explainable ML approach for frequency-response-based identification and inverse tuning of STMD.
